1. Field of the Invention
The present invention relates generally to the field of structural health monitoring (SHM) for damage detection and characterization of engineered structures, and more specifically to such systems employing a network of distributed intelligent sensors.
2. Description of the Related Art
Structural health monitoring systems (SHMSs) have been developed for many applications, from industrial applications such as monitoring manufacturing lines in paper mills to monitoring the structures and equipment of fixed wing aircraft and rotorcraft. A typical SHMS uses deployed sensors to detect the operational characteristics of various components of a system; such characteristics include the speed, vibration, and temperature of critical components comprising a system. This data is collected and stored by various means for later retrieval and analysis by experts. The experts review the collected data to determine if there are any detectable anomalies which may indicate the necessity for remedial action. Such anomalies, often referred to as “exceedances” because they exceed desired levels or characteristics, may include increasing trends in temperature over time, increasing vibration over time, or other changes in behavior which deviate from an expected baseline. For example, a change in the characteristic frequency of vibration detected from a monitored gearbox may indicate that a mechanical component such as a bearing or gear within the gearbox has failed or is in the process of failing, and therefore the system is in need of maintenance or needs to be replaced.
SHMSs as deployed on aircraft are generally referred to as health and usage monitoring systems, and are often called HUMS.
Rotorcraft such as helicopters are strong candidates for HUMS, as they are highly complex aircraft, and benefit from a HUMS ability to monitor and record engine, gearbox, rotor and other flight critical equipment so as to detect abnormalities which may ultimately effect the airworthiness of the aircraft. HUMS may also be used to monitor other flight critical parameters such as the track and balance of the rotary lift wing of the helicopter or the correct operation of the auxiliary power unit, and can include built-in test and flight data recording (FDR) capabilities.
Overall, a HUMS is intended to acquire, store, and transport data gathered from the various monitored components for further analysis so as to increase the probability that the essential components of the aircraft are operational and defect-free, and therefore that the aircraft is safe for flight. In addition, aircraft owners and operators who employ HUMS often use the information gathered by the system to establish a flight operations quality assurance (FOQA) program based on the data so as to observe trends in aircraft operations and component usage in order to improve the quality of flight operations and hence the overall safety of flight.
There are various types of HUMS which employ different methodologies and sensor types, all of which include the two basic functions of health monitoring and usage monitoring. These functions are described in greater detail below.
Health monitoring refers to the function of observing and recording the operational characteristics of principal machine components or subsystems so as to allow for remedial action should the monitored parameters not lie within a “normal” or “healthy” range. Examples of attributes typically observed and recorded during health monitoring are engine characteristics such as fluid pressures, temperatures, and mechanical vibration; transmission characteristics such as fluid pressures, temperatures, and mechanical vibrations; drive shaft balance, bearing heat and vibration, and rotor track and balance.
Usage monitoring refers to the function of observing and recording the use cases of the principal machine components or subsystems to provide for detection of situations in which the prescribed operational envelope of the component or subsystem has been exceeded. Such exceedances can adversely affect the operational life of a component or subsystem and must be taken into account when forecasting the expected remaining life. In certain situations, an exceedance of a certain magnitude may call for the immediate removal, inspection and possible replacement of a component. Examples of attributes typically observed and recorded in usage monitoring are engine torque, power and exhaust gas temperature, transmission torque and temperature, aircraft gravitational forces and loading, landing shock loads, and aircraft attitude (that is, yaw, pitch and roll angles).
SHMSs taught in prior art consist of various types of sensors mounted at points around a vehicle or equipment installation (typically near rotating components) which generate analog signals proportional to the attribute monitored, a means of transporting the analog signals from the source of generation to a digitizer usually located in close proximity to a data storage unit, a digitizer for converting the analog signals to digital signals, and a control and storage unit which receives the digitized signals and stores them for later analysis. Characteristically, the sensors contain no signal processing capability; they simply generate a temporal analog signal proportional to the attribute monitored. These signals are relayed to a central unit where they are converted to digital data and stored. Each sensor is typically connected directly to the digitizer and storage unit by a dedicated, point-to-point transmission line.
In typical prior art systems, the sensors use a simple means of detecting vibration, such as an accelerometer employing a piezoelectric substance, which detects vibration and converts the frequency and amplitude of the vibration into a very low-current analog voltage signal which is transmitted to the central digitizer and storage unit. These simple analog sensors do not have their own processing power and do not store data for later transmission. Since the sensors have no intelligence (no processing power of their own), they make no determinations relating to the severity or importance of the vibration frequency or amplitude of the signals that they generate. They simply transmit the raw, unprocessed data to a central point for digitization and storage. It is not uncommon for SHMSs of the prior art to contain dozens of such simple analog sensors, including vibration sensors, rotational speed sensors, temperature sensors, and pressure sensors, each with a dedicated transmission line running from the sensor to the system digitizer. As a result of the weak signal strength of many of these sensors, the sensors that are not located very close to the digitizer often require amplification prior to signal transmission so as to insure signal integrity at the digitizer, which adds further cost and complexity to the system.
SHMSs used to monitor aircraft in flight, particularly those systems used to monitor helicopters, face a unique set of challenges. In such systems it is required that the vibrations and torque of certain critical components be analyzed with respect to the actual flight characteristics under which the sensed data was generated. These flight characteristics, such as low speed forward flight, high speed forward flight, low power forward climb, high power forward climb and so on, may each induce different stress characteristics in the critical components which are being monitored. To understand which characteristics are normal and which are not, one needs to know the conditions under which they were generated. For example, what may be considered excessive vibration in straight and level flight may be within acceptable operational range in a high speed climb. Further compounding the problem is that, for data to be useful in comparative analysis, all of the data from a particular event should be taken during a period of time in which the flight dynamics of the aircraft are constant. Such time periods of steady state conditions are known as “points of stationarity.” Inasmuch as it is generally impractical to monitor, record, and analyze the massive amounts of data which would be generated by recording all of the data from the multitude of sensors during every moment of an aircraft in a long duration flight, data samples are only taken at predefined points of stationarity. In low cost HUMSs of the prior art, such data acquisition events are manually triggered by the pilot, generally during non-complex flight regimes such as straight and level flight. More complex and expensive systems, which include inertial sensors or which have access to inertial data on a data bus, may trigger recording events automatically when prescribed points of stationarity are detected. This is not as crucial for stationary HUMS, such as those used to record data from the machinery in a paper mill, as it is for HUMS in an aircraft such as a helicopter.
HUMS of the prior art suffer from several distinct disadvantages. Some of these shortcomings are as follows:                Wiring can be expensive, heavy, and fragile. In systems of the prior art, each sensor is wired individually to the digitizing unit, typically using a coaxial cable. The coaxial cables used are both fragile and expensive. Care must be taken in routing the cables not to bend them too sharply or compress them. If the cables are crimped or compressed they can attenuate the signals from the sensors to such a degree that the information transmitted is lost. Given that each sensor must have its own unique cable, rather than, for example, sharing a common data bus, the amount of wiring on a modern aircraft can be substantial, adding unnecessarily to the aircraft's weight.        Manual initiation of system recording can introduce both safety and accuracy concerns. In low cost systems of the prior art, the HUMS recording cycle has to be manually initiated after the pilot has placed the aircraft into a known state of stationarity, such as straight and level flight. Requiring the pilot to take such action increases the risk that the distraction so imposed might, under unique situations, (unanticipated turbulence for example) impair the safe operation of the aircraft. In addition, if the pilot misinterprets the actual flight characteristics of his aircraft (that is, the aircraft is not actually in straight and level flight for the required duration of the measurement) then the data acquired may actually be useless for purposes of aircraft health determination.        Large amounts of analog data acquired necessitate heavy, expensive digitizers and storage equipment. Because the sensors are “dumb” (no processing power), they simply record raw data and send everything over the cables to the control unit, which must then deal with this large amount of potentially rapidly-changing data.        Poor signal quality. Simple analog sensors often require the use of signal amplifiers to avoid attenuation of the signals as they travel from the point of detection to the central control unit.        Lack of adaptability. Existing HUMS cannot automatically adapt to the changing base frequency caused by a change in the base central speed, such as the engine speed of an aircraft as it moves into and through different flight regimes. Airborne systems may only be able to effectively monitor an aircraft in known and controlled flight regimes (such as hovering or on the ground).        
Several different approaches to HUMS systems exist in the prior art. U.S. Patent Publication No. 2010/0057277 by Goodrich et al. describes methods and systems for health monitoring for aircraft. Goodrich et al. describes monitoring the health of an aircraft by obtaining vibration data from a series of vibration sensors located on the aircraft, and navigation data from a navigation system (including a GPS receiver and one or more inertial navigation system, or INS, chips) and fusing the two sets of data.
U.S. Pat. No. 8,306,778 by Leao et al. describes a prognostics and health monitoring method for electro-mechanical systems and components which consists, generally, of the steps of (1) collecting performance measurements from a system component while that component is known to be healthy (in good working condition) and while the system is being commanded in response to a predetermined pattern of operation, (2) using these measurements to construct a statistical model of the component or system, (3) collecting new performance measurements from the component while it is being commanded in response to the same predetermined pattern of operation, (4) comparing the new measurements to the statistical model to calculate something called a “degradation index”, which indicates a change in performance from the statistical model, and (5) using the trend of the degradation index over time to predict a future failure of the component. Leao appears to be describing a simple closed loop monitoring system.
U.S. Pat. No. 6,289,735 by Dister et al. describes a system and method of vibration analysis. This patent describes receiving vibration signals from a vibration sensor mounted on a machine, and then using a known critical frequency (for example, a critical frequency supplied by a manufacturer for a rotating component) to analyze the amplitude of the vibration signals at several harmonic frequencies calculated from the known critical frequency.
U.S. Pat. No. 6,321,602 by Ben-Ramdhane describes a method of condition-based vibrational analysis wherein the condition of a bearing or shaft assembly is monitored by obtaining a first spectral analysis of the assembly's vibration, and then comparing this baseline spectral analysis to subsequent spectral analyses of assembly vibrations obtained at a later time to determine the condition of the assembly.
U.S. Pat. No. 6,711,952 by Leamy et al. describes a system and method for monitoring bearings that is suited particularly for use in an aircraft gas turbine engine, where mounting a vibration sensor close to the rotating shaft is difficult because of the high temperature environment near the shaft. Instead, Leamy et al. mounts the sensor remotely, proximate to the rotating shaft but not in the high-temperature region. The remote sensor then obtains a broadband signal which contains frequencies that include the “bearing defect peak,” and if the peak is detected the amplitude of the peak is quantified to determine if degradation of the monitored bearing has reached a threshold level where imminent failure can be detected and prevented.
None of the systems described above use intelligent sensors (that is, vibration or other HUMS sensors which have a significant amount of processing power integral to their bodies), cannot automatically detect flight regimes, and do not automatically adjust the range of “frequencies of interest” based on a key system frequency signal.
What is needed in the art is a health and usage monitoring system in which the processing power is distributed through the use of intelligent sensors, which can automatically detect when the aircraft has entered into a flight regime for which recording of data is required, and which can automatically adapt to changing key frequencies on the vehicle. When a large vibration amplitude is discovered at one of the harmonic frequencies, that indicates that a resonant frequency of one of the transmission paths from the vibration source to the vibration sensors has been located, and this information can be used to analyze the health of a particular component.